A Tale Of Two Convergences

In the course of doing the research for my previous post on thunderstorm evaporation, I came across something I’d read about but never had seen. This was the claim that the models showed not one, but two inter-tropical convergence zones (ITCZ).

Please allow me a small digression here, regarding my unusual methods of study and investigation. Faced with information about the possible existence of a dual intertropical convergence zone in the models, most scientists would start by going out and researching the question in the scientific literature. First they would find out everything that is known about the models having two ITCZs. They would study what other people have written about double ITCZs. They would read what people believe are the causes of the dual ITCZ. Then, and only then, would they take up independent research.

Me, I approach a new subject the other way ’round. I don’t want to start out already knowing what other people have written about double ITCZs. I don’t want to know the various theories about them when I begin my investigation. I don’t want to understand what scientists say are the causes before I begin. Instead, I want to start out without preconceptions, without prejudices, without formed ideas of what I will find. The Zen Buddhists have a lovely term for this condition. They call it “beginner’s mind”, and they strive to achieve and maintain it. Starting my investigations with beginner’s mind forces me to invent my own methods. It makes me have to dig in deeply to understand what I’m looking at. And most importantly, it shuts no doors, it rules nothing out, it leaves intact my awe and wonder at the myriad possibilities of the amazing system I’m studying. I like to start out with the clear understanding that I understand nothing. It is a huge advantage if you wish to discover new things.

Once I have formed my own ideas about what is going on, once I’ve graphed and mapped and sliced and diced the data, once I’ve pondered the findings and the graphics to the point where I think I understand the relationships and the implications, then and only then do I go and read what other people have said about the subject.

Now, I understand that there are a lot of folks who do it the other way. And I have no problem with that. However, I do get flack for the way that I do it, people insisting that I should always start by studying what is known before I start my own investigations. Sorry, not my style. Especially in climate science, what is “known” is far too often not true in the slightest. I don’t want to be burdened with that. As Mark Twain, one of my lifelong literary heroes, said:

It’s not what you don’t know that kills you, it’s what you know for sure that ain’t true.

Now, I’m aware that sometimes my way leads me to error … and given the number of folks who notify me whenever such an error occurs, I could hardly be unaware of it.

But the other way often leads to error as well. Worse than that, however, is that it leads to a hidebound view of the subject, one which has already closed a host of doors and ruled out a host of possibilities. If someone doesn’t do independent research into a subject until AFTER they have been totally indoctrinated into whatever the current view of the subject might be, they’ve already put the blinders on. They’ve already taken up the current paradigm and the current frame without even noticing it, and sadly, that means that they are very unlikely to ever discover anything outside of that frame and paradigm … but I digress.

As you might imagine given my method described above, my first move was to go grab the rainfall data and consider the ITCZ:

One of the surprises to me when I first graphed this up were the large areas of ocean in both the northern and southern hemispheres which get little or no rainfall. I never considered that there would be oceanic “deserts” of such size and scope.

The ITCZ (inter-tropical convergence zone) can be seen clearly in the TRMM average rainfall record. It is the yellow/red area that runs along and generally above the Equator. As the name suggests, the ITCZ is the area where the winds of the two hemispheres converge.

Next, I got rainfall data from a random model to see what the ITCZ looked like in the model output. It’s the bcc-csm1 model, chosen because they were listed alphabetically and I took the first one. I’ve trimmed the model output to 40°N to 40°S to match the TRMM data to allow easy and accurate comparison.

Yikes! … I’d sure call that a “double ITCZ”, all right. I can see why this would be very worrisome. The model is claiming extensive rainfall in parts of the central Pacific where in fact there is almost no rain at all. For example, in the observations, the ITCZ in the Pacific has dark blue areas of no rain both above and below it. The model has neither. I also note that the model says that there is about 20% more rainfall in the covered area than the observations show. Finally, I note that this gives a modeled evaporative cooling about 15 W/m2 larger than in the real world. Errors like these make me laugh at the claims that the models can show the results of a change of a few W/m2 in the forcings … but once again I digress …

To gain a better understanding of the two convergence zones, I made a movie loop of the monthly modeled rainfall so I could compare it to the movie loop of actual rainfall that I showed in the last post. Here are those two movie loops.

Yikes again! It’s interesting. Even better, it’s not at all what I expected, which is the most fun part of science. Actually, the model appears to only have one ITCZ … but the model ITCZ spends half the year north of the equator and the other half south of the equator. The net result looks like two ITCZs, but it’s not.

The problem appears to be that the model is too symmetrical. As a result, the ITCZ is pulled evenly first north and then south of the equator. But for some unknown reason, that doesn’t happen in the real world. [Curiously, in both cases the ITCZ never occurs right at the equator. It may have something to do with the existence of the equatorial counter-current, which runs along the equator in the opposite direction to the waters just north and south of it … but that is just speculation.]

I can see that this would be a particularly thorny problem to solve. It’s one of the difficulties with iterative models. You can never be sure in advance what some small change might do. And in particular, this asymmetric oddity would require unknown pressures to maintain itself. Hard to even guess where and why the models are wrong, when as far as I know, we don’t know why the real ITCZ isn’t symmetrical in the same way that the modeled one is. (In passing, to me this is the real value of models—to point out the interesting areas where the world does NOT agree with the models.)

Now, I’m sure there’s more to learn in all of this investigation of modeled rainfall. And normally, this would be nearing the point in my investigation where I would go Google “double ITCZ climate models” or something and read some of the literature … but why? For me the model results are meaningless. Almost all of the active temperature-regulating emergent phenomena are far smaller than the model gridscale. So phenomena like thunderstorms, dust devils, tornadoes, and tropical cumulus are not modeled, they are merely parameterized … and those are the very phenomena which keep the global surface temperature regulated between narrow limits (e.g. ± 0.3°C over the 20th century). In my opinion, the lack of such sub-gridscale phenomena is why the modeled rainfall patterns over the ocean are so bizarre. Without the details of how and where and when the thunderstorms emerge, it would be hopeless to try to model oceanic rainfall.

Since the models don’t explicitly model the very phenomena that keep the world from overheating or excessive cooling, the model results are useless to me. So I try to not waste too much time on them.

The results show that models perform poorly, even at a climatic (30-year) scale. Thus local model projections cannot be credible, whereas a common argument that models can perform better at larger spatial scales is unsupported.

Can’t say fairer than that …

My best to everyone,

w.

My Usual Request: If you disagree with me or anyone, please quote the exact words you disagree with. I can defend my own words. I cannot defend someone’s interpretation of my words.

My New Request: If you think that e.g. I’m using the wrong method on the wrong dataset, please educate me and others by demonstrating the proper use of the right method on the right dataset. Simply claiming I’m wrong doesn’t advance the discussion.

I am not aware of anywhere that gets 4 meters of rain in the US. Of course that could just be my ignorance. I guess the Olympic Rain Forest might get that much, but somewhere on the front range of the Colorado Rockies? That doesn’t seem credible.156 inches is a lot of rain outside of the tropics. Hawaii has some places that get 200+ inches a year, Alaska has some that get 140+ inches a year, some places in Oregon get in the ~120 inches per year range, some places in western Washington ~100 inches per year, a couple of places in New Hampshire average in the 90s inches per year, a couple of places in North Carolina in the low 90s, one place in Northern California gets in the 80s. There is no location in the mountain west with rainfall totals above 35 inches a year. The source for this is http://rainfall.weatherdb.com/

Willis, I think Anthony is right. Don’t let someone else incorporate your ideas and take the credit. You’re already a published, peer reviewed author. Make this another paper to your credit. That’s just MHO…

“Now, I understand that there are a lot of folks who do it the other way. And I have no problem with that. However, I do get flack for the way that I do it, people insisting that I should always start by studying what is known before I start my own investigations. Sorry, not my style. Especially in climate science, what is “known” is far too often not true in the slightest.”
I remember when some here were giving you a lot of flak about “claiming” that you “discovered” something, then saying you’re an egomaniac that hasn’t bothered to study the literature.
I’m a scientist, and I say just the opposite. The flak is ego driven. They hold themselves in such high regard because they have read so much of what others have done. They haven’t attempted to replicate any of it, but merely reading the literature makes them “real scientists”.
Your work is much more valuable, because you provide an independent validation or contradiction of what others have studied before. That’s real science, not the herd mentality that passes for science these days.
Kudos.

Concur. A nice example of models geting it wrong.
The model double ITCZ you were recalling was from my guest post here earlier this year, The Trouble with Models. DoE’s CAPT program was one of two formal US government initiatives for improving GCM parameterizations. Parameterizing away a ‘true’ double ITCZ (because there isn’t one in the real world) was the first example in NASA’s brag paper about the process, the example cited in the guest post. You can download the paper at http://www.ceres.larc.nasa.gov/documents/STM/2004-11/Xie.pdf.

The period (.) following ‘pdf’ is treated as part of the url, which is an error. Additionally, when I tried copy/pasting the address, the slashes (/) and the hyphen (-) were not carried over leaving spaces in their stead. Fix these copy errors and you’re good to go.

So if the Northern Hemisphere behaviour does not, in reality mirror, the Southern Hemisphere behaviour in this respect then it must be due to one of the differences between the two hemispheres.
Either:
A) Width of the Ocean in that hemisphere.
B) Amount of land in that hemisphere.
C) Nutrients from rivers and volcanology in that hemisphere.
D) Proximity of the Earth to the Sun in Summer.
E) Something else.
Can we brainstorm to fill out E?
Can we reject any of A, B C or D?

E) will probably include things related to A) thru D). For instance, albedo relates to A); plankton levels relate to C). E) could include thickness of the ionosphere, possibly related to the Earth’s magnetic field. I can’t rule any of A) through D) out, though B) is obviously a function of A).

Thanks Willis for yet another thought provoking post which perfectly illustrates the stupidity of using flawed models by our badly advised lawmakers.
Figure one triggered a thought that the main centers of precipitation are probably caused by transpiration from rain forests. These centers being SE Asia, Central Africa and Amazonas. The produced moisture is then distributed far across the globe. Perhaps climate science under-estimates the effects of our biosphere on global precipitation by orders of magnitude.

F) That bloody great mountainous desert of ice. The worlds highest continent, surrounded (As you do say!) by the worlds greatest expanse of open ocean might also have something to to with it but I’m justing thinking out loud! 😉

This is yet another compelling post by Willis.
I’m a very visual person and also have my own idiosyncratic approach to life.
I believe our maps may at times, betray a cultural bias.
I had an interest in space as a kid and I used to say that if you visited the Earth from a direction directly above the Pacific, all you can see is water! (But that is for another projection.)
Meditating on Willis’s animations, I was excited by the idea of seeing them longitudinally, as a Cassini projection. Being a spatial thinker I wished to see them in a way that might help me understand the processes better.
I understand that full data coverage may not be available but I think it would be revealing to visualise the worlds climatic zones, “breathing in and out” in a way that doesn’t privilege latitude. I do appreciate that the post was about the equatorial zones, of course! 😉http://i66.tinypic.com/20rmsg6.jpg

I may have missed something but the annual average plots suggest that you are comparing TRMM data from 1997 – 2015 with simulated data from 1850 – 2012. If that is what we are looking at I don’t think it’s reasonable to expect them to match up terribly closely and I’m not sure it’s possible to draw any reasonable conclusions from the comparison without presenting additional analyses (synoptic structures, etc).
And if the monthly average values are also from the same time periods for each dataset I don’t think that’s an entirely fair or defensible comparison either.
If it’s possible to trim the two datasets so they both show results from 1997-2012 I think this would be considerably more instructive.

If I had done this, after finding the first model deficient, I’d have tried a few of the other models as well to see if any did match reality. A sample of one is not likely to be representative of all the models.
I would also like to see a loop of the individual years since an average may hide some effects.

If I had done this, after finding the first model deficient, I’d have tried a few of the other models as well to see if any did match reality. A sample of one is not likely to be representative of all the models.

Thanks, Jeff. I’d do that myself … if I thought there was any cheese at the end of that particular maze. But I already know from a host of other evidence that the models are totally inadequate for the purposes they’re put to, so I choose not to waste my time demonstrating that one more time.
w.

The TRMM data seem to show a band of rainfall appearing very briefly just below the equator around February or perhaps March. The animation moves too fast for my feeble eyes to make it out clearly just when it occurs. Perhaps that is the phenomena the model is trying to reproduce?

Those who ‘play’ at science often labor under the misapprehension that the purpose of a model is to predict the real observations with high accuracy. This is a pitfall of misunderstanding, at the bottom of which one finds only confirmation bias.
The true purpose of a model is to test our understanding of reality. If the model does not match the reality, a scientist assumes the *model* is in error, and by examining the differences seeks to find the disconnect between the two. By developing NEW hypotheses the scientist refines and then further tests the model to learn something NEW.
To say ‘l learned something new’ is much more enriching than to say ‘I was right all the time.’

The problem comes when ideological, such as the Gorestapo, attempt to shut-down actual knowledge by pointing to their proven-failures of models and declare that the data from them are sound, when the opposite has demonstably proven, time and again.

“To say ‘l learned something new’ is much more enriching than to say ‘I was right all the time.’”
You mean like something new that doesn’t happen on earth?? As they continuously use the models to try and justify their political allies agenda it is necessary to discover something NEW that actually happens on earth so they can actually model the earth system well enough to make projections relevant to the purpose for which they are primarily used, supporting, or not, mitigation efforts.
Learning something new is relatively pointless as we still don’t know why it does or does not apply to the earth.

“As a result, [in the model]the ITCZ is pulled evenly first north and then south of the equator. But for some unknown reason, that doesn’t happen in the real world”
I believe the N-S assymmetry in the ITCZ is thought to be due to the asymmetrical continental masses N and S of the equator – unfortunately I forget the details.

“[Curiously, in both cases the ITCZ never occurs right at the equator. It may have something to do with the existence of the equatorial counter-current, which runs along the equator in the opposite direction to the waters just north and south of it … but that is just speculation.]”
WR: The Pacific counter current – as far as I can see at maps – runs at 5 degrees north. However, the blue region is at the equator, more to the south. Relative cold water corresponds better with the blue rainless area: see sea water temperature maps. In other oceans the pattern the rainfall area does travel across the equator.
Further: the definition of Wikipedia of ITCZ is about winds and not about rainfall.https://en.wikipedia.org/wiki/Intertropical_Convergence_Zone
“The Inter Tropical Convergence Zone (ITCZ), known by sailors as the doldrums, is the area encircling the earth near the equator where the northeast and southeast trade winds come together.”
and:https://en.wikipedia.org/wiki/Doldrums
“The doldrums is a colloquial expression derived from historical maritime usage, in which it refers to those parts of the Atlantic Ocean and the Pacific Ocean affected by the Intertropical Convergence Zone, a low-pressure area around the equator where the prevailing winds are calm.”
WR: interesting again. The doldrums area – where trade winds come together – is known by less or no wind, at least during longer periods. As I read in your post before, Willis, thunder storms create wind at the surface. The situation in the Pacific above the blue zone of (I think) cold water tells once again that relative cold water does not create thunderstorms. And doesn’t have much wind – doldrums.
On the other hand: colder subsurface water comes up when wind blows the warm surface water away. From this point of view it is logic that you can find the cold water strip in between the two rain zones where the wind is drawn to by the rising air of the thunderstorms in those rainy zones.
Remains the question: why are there in the Pacific TWO rain zones.

Researched this for reasons different than Willis’ starting point. ITCZ is not just rain, it is a very satellite visible ‘permanent’ cloud band. Its seasonal undulations are responsible for the Asian monsoons, the wet and dry seasons in Amazonia, and for the two rainy seasons in Kenya (the longer crop nourishing one being the MAM rain). None of which the CMIP5 archive gets anywhere close to right.

Willis,
You’ve compared a model with a satellite measure, TRMM, and decided the model is wrong. As hipper noted, the time periods are different. But how about some ground truth?
I think Tahiti is among the dots on about a level with Cape York, and East of Hawaii. TRM shows it well beyond the ICTZ, and pretty dry, on the border of blue (.8 m) and dark blue (0). BCC shows it on the southern fringe of a wet region, 2.4 (W/m2?? – are the units right in either plot?).
Papeete gets about 1.8 m/yr.

Willis,
You’ve compared a model with a satellite measure, TRMM, and decided the model is wrong.

Nope. I’ve compared a model with everything that I know about the ITCZ from a host of sources including satellite data, and decided the model is wrong. That is, unless you are claiming that the real world actually does have the “dual ITCZ” pattern shown by the model …
w.

In the early 80’s I worked for a large consulting firm. My boss was a physicist. When discussing with a client an area in which he did not have extensive knowledge, he had a great line: “I am not burdened by an excess of knowledge in that area.”

Two things, Willis;
First, I had never heard of the “beginners mind” before but the concept is my own approach to anything that I find doesn’t make sense.
Second, Anthony is right, you have to start publishing these things as scientific papers, otherwise someone else will pick up the idea and get the kudos.
We here will know, but lots of other folk won’t have a clue and the thief (not too strong a term) will get the credit.

Willis, “ditto” to Oldseadog. A paper will make it harder to ignore your points too.
Because the stakes are so high I want you to be “heard”. The voting public needs to realize there is no science to support the GHG. Theories explain the data, modern Climatology explains how to steal trillions.

Good one Will. This explains what I saw and referred to in my comment to you in your previous post. Ie the sine wave appearance….frequency and periodicy. I mistakenly took the cycle above and below equtora as true, whereas you took it as an opportunity to test validity. Nice one.

Maybe the below would apply to Willis’ post concerning why it appears there is more rainfall above the equator at the ITCZ. Sounds reasonable.
By thx1138 from Google answers:
The answer is not really the complex atmospheric patterns, it is the
fact that the southern hemisphere has more area of ocean than the
northern hemisphere, thus when the Earth is closer to the sun the
southern oceans absorb that extra energy more than the in the northern
hemisphere where there is more land area.
See:
“There is more land in the Northern Hemisphere, and more water bodies
in the Southern Hemisphere. Now, land has a much lower specific heat
capacity than water; in other words, water can hold a lot of heat
while land cannot. Hence, land gets heated up faster and also cools
faster than water. So, during summer, the greater amount of land in
the northern hemisphere gets heated up quicker, while in the southern
hemisphere, the water soaks a lot of heat and gets warmer by a much
lesser amount. In any case, the result is that northern summers are
hotter than the southern summers”http://curious.astro.cornell.edu/question.php?number=180
also:
“Even though the difference between the earth?s perihelion and
aphelion distances is less than 3%. The amount of solar energy
striking the earth is 7% greater at perihelion (in January) than at
aphelion (in July). This would lead one to conclude that summer in the
southern hemisphere, which occurs at perihelion, is warmer than summer
in the northern hemisphere. This, however, is not the case. Most of
the land mass of the earth is concentrated in the northern hemisphere.
The southern hemisphere, by contrast, is 80% covered by water. Water
has the ability to absorb large amounts of heat. The additional solar
energy supplied by the sun at perihelion is absorbed by the large
bodies of water in the southern hemisphere. The result is that
temperatures are actually more moderate during summers in the southern
hemisphere. On Mars, which does not have any oceans to absorb heat,
the temperature fluctuations are much greater due to perihelion and
aphelion.”http://www.physics.isu.edu/~hackmart/astlbsol.pdf
and:
“But that’s not the whole story. Earth is warmer overall in July due
to the unequal distribution of land on the planet. Oceans and
continents are not distributed evenly around the globe, so the summer
sun beating down on the extensive land in the Northern Hemisphere
raises the temperature more than it does in the Southern Hemisphere
six months later.”http://starbulletin.com/2002/07/07/business/brill.html

Before you try to write a paper you might want to actually read the literature, because there are a number of papers that do discuss the double ITCZ model issue. This is not a new finding, and no one is pretending that the models are right and the observations are wrong. I understand the desire to not pre-bias yourself, but this is an exceptionally poor way of doing science. We’d never get anywhere if everyone just ignored all previous work on the subject.

I’m not sure why you would put “not right” in quotes when I didn’t even say “not right.” When models and observations do not match up, scientists try to figure out why. My point was that they don’t throw out the observations (assuming they were properly collected) and just go with the models, while ignoring the real world. Models help to teach us what we don’t understand. And we depend on models for future decision making because unfortunately we don’t have time machines to collect future observations. Just because a model is not perfect does not mean it’s completely wrong or we shouldn’t take any action. I’m sure you’ll continue to twist my words on that topic, but my main point was that the double ITCZ is not a new feature that is just being discovered. Reading the past literature is necessary to understanding any scientific process.

pd2413: “My point was that they don’t throw out the observations (assuming they were properly collected) and just go with the models, while ignoring the real world.”
Unfortunately, in the field of climate “science”, that is exactly what they do.
I mean what climate “scientist’ would believe a $10 thermometer when it disagrees with a $100,000,000 computer game climate model?
Here you go, straight from the horse’s mouth.
“The data doesn’t matter. We’re not basing our recommendations on the data. We’re basing them on the climate models.”
~ Prof. Chris Folland ~ (Hadley Centre for Climate Prediction and Research)
Don’t forget, we’re talking climate “scientists” here, not engineers.

@pd2413 “When models and observations do not match up, scientists try to figure out why.”

None of the models used by IPCC are initialized to the observed state and none of the climate states in the models correspond even remotely to the current observed climate. In particular, the state of the oceans, sea ice, and soil moisture has no relationship to the observed state at any recent time in any of the IPCC models.

“…no one is pretending that the models are right and the observations are wrong….”
Ah, that’s not so. The models are being used to sell political action, while observations are being explained away with specious mathematics and statistical methods.

So we’re at the point where you just mock rather than use facts. But apparently, while I was in my long coma, multiple climate scientists were studying why the models do not properly simulate the ITCZ and instead show a double ITCZ. They know that the ITCZ is an incorrect output and are attempting to determine what is causing the discrepancy and improve the next iteration.

pd2413 – “Just because a model is not perfect does not mean it’s completely wrong or we shouldn’t take any action.”
Oh dear. “Precautionary Principle”?? Doubled down?
Instead, I think we should follow “Darwin’s Principle” and adapt. If the seas rise 10 metres in 10,000 years, I think I can live with that. 🙂

I looked at the first linked paper in your list and there were no solutions. Nothing definitive, just correlations and theories about components likely to be at fault. Clouds mostly..surprise, surprise… and then statements along the lines of needing to do more work to narrow the causes down.
Your defence here reminds me of the dendro divergence problem, actually. For years AGW enthusiasts responded to the divergence problem with a reference to a paper discussing it published in, from memory, 1998. With an assumption that it was all explained in there. Except it isn’t and here we are nearly 20 years later and still no explanation for divergence. Just theories.

“…We’d never get anywhere if everyone just ignored all previous work on the subject…”

And the obverse would be? Everyone should always accept prior science and build upon it?
A) All science should be independently replicated, multiple times.
B) Since when are ‘models’ science?
The idea is that models should replicate reality. When models fail to replicate reality, they are definitely not models in a scientific aspect; though they might be models in a childish play model.
Willis never claimed to not read the literature. He didn’t read the literature first. A practice more scientists should try as the climate consensus is a long way from observations.
A reality most engineers can readily inform consensus climate science. Building upon improper or poor foundations is a recipe for disaster. Real world engineers get sued for bad research or foundation designs. It will be nice to see climate science treated equitably. Perhaps more climate scientists will approach science with open minds.

pd2413
Willis’ way, as he explained it, allows him to delve into a subject without prejudice and then, when he understands all he can about the subject on his own, can then look at the prior research and findings, and be better able use the prior information without bias and prejudice and to throw out what is useless and retain what is useful. It is not, as you state, simply “ignoring all previous work”.
When one follows the much used path through the forest, one doesn’t get to see and observe what is happening on the unbeaten trail or the other paths. If you simply follow other peoples trails, without exploring and creating your own, you will probably only see the same thing as the people you are following. When you come out of the forest, you have, not only the information from those others who proceeded you, but also your own, unbiased, firsthand observed, information with which you can then compare and either use or discard the useless and come to your own conclusion, free of anyone elses opinions or conclusions.
That make sense?

One key facet of science that is too often lost in the modern herd mentality is that nobody should ever have to trust someone else. They should be able to reproduce the study for themselves, using the methods and analysis put forward in scientific papers.
Of all the papers you’ve read, how many have you attempted to replicate for yourself? Half? Far, far less than that?
Willis is a throwback, wanting to settle issues in his own mind and through his own experimentation. Most of the herd can’t be bothered to replicate or even to question the findings put forward by others. There could be a cursory review of the data and methods, but no attempt to replicate or validate them. In almost all cases, the results are accepted unquestioned. I’ve attended two high profile talks by visiting professors who’s work was later discredited. One actually received a standing ovation at the end of this presentation, which is quite unusual. Within a year he was retracting the work and trying to shift blame onto his collaborators. But all the scientists in the room were accepting this highly flawed work unquestioned, because that’s what modern science is.
My 5th grader does more scrutiny and replication of scientific knowledge in his elementary school science fair than happens at Universities. Is he wasting his time re-inventing the wheel? NO, he is being a real scientist, taking the things he has been told about the world around him, and testing it for himself. This integral piece of the scientific process has been lost somewhere along the way, and we see the consequences when so much “peer reviewed” work can’t be replicated.

Yes, yes, yes. This is exactly how one should start to do model validation. Start with large scale phenomena and data that are well documented and make sure you can do that correctly. At the very least, you cannot accept model behavior that does not occur in the physical system. Until you can model the grand parameters, you have no idea whether the fine features are correct. It means that you have bad internal models, and you have to fix them until you get the overall behavior correct. Then you have to iterate on all the rest of the parameters.
This is the smoking gun of climate “science”. It does not work. It is NOT settled.

pd2413 November 12, 2015 at 3:54 pm
That’s exactly what they do, which is why there is literature on that exact problem with the models. Mr. Eschenbach is by no means the first person to have noticed a double ITCZ.
Your statement makes no sense.
First Willis never claimed to be the first person to spot the double ITCZ
Willis:
In the course of doing the research for my previous post on thunderstorm evaporation, I came across something I’d read about but never had seen. This was the claim that the models showed not one, but two inter-tropical convergence zones (ITCZ).
note the words” I CAME ACROSS”…
He acknowledged it was known.
Perhaps you may wish to go back to the article re-read it, think about it for a time and then try again.
pd2413 you have a brain use it.
michael

Willis,
I echo the sentiments of the others here… your last couple of posts really deserve to become published papers.
Another source that I watch is the worldwide lightning locator network at….http://wwlln.net/
They use over 50 ground based VLF sensor locations to detect, locate and record lightning strikes from thunderstorm activity around the world. I’m not sure if you are aware of them. If not their data may also prove of some value to you.

Wills wrote: “Once I have formed my own ideas about what is going on, once I’ve graphed and mapped and sliced and diced the data, once I’ve pondered the findings and the graphics to the point where I think I understand the relationships and the implications, then and only then do I go and read what other people have said about the subject.”
Are you missing a sentence that could follow? “Then I try to understand why I came to a different conclusions from others.”

When I saw Willis’ chart on rainfall yesterday I thought “Wow, I would not have expected that.” What I expected is exactly what the model shows. Conclusion: Models simply confirm the ignorant prejudices we have when we don’t actually perform observations.

P. Wayne Townsend: “Models simply confirm the ignorant prejudices we have when we don’t actually perform observations.”
Of course they do.
They reflect the prejudices of the person who hired the programmer, or else the programmer won’t get paid.
Programmers have to make a living too, you know!

Willis wrote: “I also note that the model says that there is about 20% more rainfall in the covered area than the observations show. Finally, I note that this gives a modeled evaporative cooling about 15 W/m2 larger than in the real world.”
And later: “Now, I’m sure there’s more to learn in all of this investigation of modeled rainfall. And normally, this would be nearing the point in my investigation where I would go Google “double ITCZ climate models” or something and read some of the literature … but why? For me the model results are meaningless. Almost all of the active temperature-regulating emergent phenomena are far smaller than the model grid scale.”
One reason for following up on your observation would be that this climate model must be compensating for the excess evaporative cooling somehow. 15 W/m2 is an error – equivalent to the change in OLR from a 3 degC change in surface temperature – assuming DLR doesn’t increase (which it will). Perhaps an increase in cloud cover compensates, but this should produce an incorrect albedo. All models are tuned so that outgoing OLR and reflected SWR agree with climate models.

Before you try to write a paper you might want to actually read the literature, because there are a number of papers that do discuss the double ITCZ model issue. This is not a new finding, …

I started the paper by saying that I had read about the double ITCZ model, so OBVIOUSLY it is not a new finding, nor did I claim it was a new finding. You’re attacking a straw man.

… and no one is pretending that the models are right and the observations are wrong.

Since people are claiming on the basis of these same models that we need to totally re-structure the global economy, yes, pd, people ARE “pretending that the models are right”.

I understand the desire to not pre-bias yourself, but this is an exceptionally poor way of doing science.

Given the number and the extent of the interesting discoveries that I’ve made using exactly this method, I can only conclude that it is an exceptionally good way of doing science. Take my work on extinctions. I never could have found out what I did if I had first absorbed the conventional wisdom on the subject. When I first wrote about extinctions, everyone was convinced we were in the so-called “Sixth Wave of Extinctions”.
Being free of such preconceptions allowed me to question the bogus claims and to determine the underlying truth. The peer-reviewed paper that Dr. Craig Loehle and I wrote on the subject has now garnered over thirty citations in other scientific journals.
So I fear that the reality of my original work disproves your claims that my way is a “poor way”. Look, I’m not recommending it for other people. It might indeed be a very poor way for you to do science, I don’t know. Everyone has to work in the way that they find best. However, this is what works for me, and it has worked exceptionally well for me.

We’d never get anywhere if everyone just ignored all previous work on the subject.

I never said that we should “ignore all previous work on the subject”. I said very clearly that at a certain point in my investigations, I look at what others have written on the subject. I’m just careful not to do it until I’ve taken my own look, free from pre-conceptions, and drawn what insights I can from the data itself.
You see, when I start my investigation, I’m not interested in what PEOPLE have to say on the subject. That comes later. Instead, when I begin my research I’m only interested in what the DATA has to say about the subject.
Regards,
w.

“They didn’t know it was impossible. So they did it.” -Mark Twain
I like this quote. I also like to try things on my own first to see what I can see. For me, if I read anything about what other people have tried it biases my brain somehow. I seem to learn more from their work if I read about it after I try it myself. Not ‘pre-biasing’ oneself is an excellent way to do science. Read what other people have done only after you exhaust your own ideas first. You’ll never get another chance to be totally unbiased after you read their ideas.

Willis: On the subject of “Zen Mind, Beginner ‘s Mind”, your method of learning and approach to scientific research is also the one I’ve used very successfully myself for over 30 years and it’s resulted in several papers that changed the field I worked in.
Quite a few scientists brought up in academia (as opposed to being born that way) end up being what I call “puzzle solvers” who take the existing body of work and expend great effort filling in blanks. It’s a necessary activity in mature disciplines I suppose, but I can’t be sure since I’ve never worked in a mature discipline 🙂 I don’t think it’s very useful in the development of new fields if for no other reason than there’s far too much cruft and wild speculation going on so spending a great amount of time reading detailed accounts of other failures isn’t very productive; like you I prefer a fast overview of current thinking to make sure I’m not replicating one of those failures, then set off on my own line of inquiry. After I’m done, I search for other work that might either reproduce what I think I’ve discovered or refute it in a believable way. I find that approach most economical and also more fun.
Good article BTW.

The problem appears to be that the model is too symmetrical. As a result, the ITCZ is pulled evenly first north and then south of the equator. But for some unknown reason, that doesn’t happen in the real world.

My guess would be that the model doesn’t get right the Milankovitch precessional cycle forcing. The position of the ITCZ is determined by the thermal latitudinal gradient, and moves slightly North and South with the seasons producing the summer and winter monsoons. The thermal latitudinal gradient that determines the position of the ITCZ is affected by the different insolation at both hemispheres due mainly to the precessional cycle. As the Holocene has advanced, the northern summer insolation has decreased and the northern summer position of the ITCZ has moved southward putting an end to the African Humid Period. But it still favors that the ITCZ spends more time in the northern hemisphere.
All models appear to underestimate effective solar forcing and the incorrect position of the ITCZ is just one of many indications of that. The modeled ITCZ is moving southward too much during the austral summer.

Willis
Did you check the TRMM average rainfall record? It does not look right to me.
For example, India has on average just under 1.8m of rainfall, and yet on the TRMM plot it is shown dark blue (ie., 0m, at any rate less than 0.8m). Putting more detail, the South such as Goa and Mangalore have some 2.8m to ~3.5m of rainfall, ie., the South should appear yellow and orange, and at best one can see a little bit of green. See for example the detailed summary on India:http://www.india.climatemps.com/
I consider that the TRMM plot needs to be cross checked for accuracy.

I am talking about this map [Figure 1. Annual average rainfall from the Tropical Rainfall Measuring Mission (TRMM)], which I understand represents annual rainfall. India is the triangular shaped country, which in my eyes is shaded in dark blue variably light blue, with the little island (Sri Lanka) at its southern most tip, and is situated to the right of Africa. The Indian ocean is shown in green.https://wattsupwiththat.files.wordpress.com/2015/08/trmm-annual-avg-rainfall-1997-2015.png?w=720

Willis
if you look at ClimaTemps (http://www.india.climatemps.com/) , it lists 42 locations in India in which rainfall is actually measured. The wettest place (Cherrapunji) has a subtropical rainforest climate with over 11m of rainfallannually! The average for India is 1.8m, and given that average, one might expect that there would be plenty of places that have an annual rainfall of about 1.6m annually (green), and the data at ClimaTemp confirms that there are many such places.
No doubt the TRMM plot is low resolution, but to my eyes it does not seem to correlate well with the actual measurement data set out at ClimaTemps.

That has to be a problem with the way TRMM collected and processed the returns (mission ended April 2015). The data I posted earlier shows the Hawaiian mountains getting about 5 meters per year and they show up in the 0.8-1.6 m/year color in the data as well. Though the color for Guam may be close to its recorded rainfall. I have a feeling the pixel resolution for TRMM may be even larger than the model’s resolution.(I looked it up – it is 0.25X0.25 degrees, or 15X15 minutes.) The problem we are noting is likely associated with the note in the mission statement below. (from https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission)

Both the Indian coastal mountains and the area of the Hawaiian Islands would fit that description well, but they seem to think the ocean areas should be fairly useful. We’d probably have to compare to the rainfall measurement capable TAO buoys to see if that assumption is close. I would hope the program office had checked their rainfall model against a few rain gauges to verify the assumptions, but I have seen too many models divorced from reality in climate science to make that assumption. OK, I researched that and discovered that from 1997-2000 they did that experiment to calibrate TRMM (details at http://www.pmel.noaa.gov/tao/proj_over/trmm/trmm.html),so open ocean areas should be good data.
Funny what you learn as you look into problems.

Makes sense, but it goes to show the difficulty in modelling the real world.
One significant problem is that in any grid cell, the energy in does not equal the energy out since much energy is transported all around, indeed actually in 3D since the energy is not simply being distributed horizontally, but also vertically.
One only has to look at the equatorial and tropical oceans that are powerhouse of the planet’s heat pump to see that there must be massive amounts of evaporation over these oceans but over large areas of the ocean from where the evaporation takes place, there is very little rainfall, so one can see how energy and heat is being both dissipated and distributed from one p[lace to another.
Given the importance of the latent heat exchange and the contrast between dry and wet adiabatic lapse rate, and the manner in which evaporation drives cloud formation and indeed varying degrees of cloud albedo (which varies according to the nature of the water vapour, droplets, crystals that are held) getting the water cycle right at small grid cell basis is an imperative if there is to be any prospect of modelling the real world.

There are some issues with the TRMM Rainfall Map image in your post.
Western Indian Peninsula receives much higher rainfall than depicted. North India receives much less rainfall than 3.2 to 4 m/yr shown in the image.
Here is a time average map of precipitation rate monthly in mm/month (40N to 40 S for the world)
This map uses precipitation rate TRMM 3B43 v7http://www.gujaratweather.com/wordpress/wp-content/uploads/2015/04/GIOVANNI-outputmdJiNsMq.png
“Analyses and visualizations of the image used for this comment was produced with the Giovanni online data system, developed and maintained by the NASA GES DISC.”

I don’t know Ashok Patel:
Your map has a great many similarities except the coastline and high Himalaya’s.
Another difference is that the map Willis provides is Average annual rainfall. The map you provide is a monthly average. I would expect that your map summed into an annual would look just like the one Willis provides.

On a water world whose oceans drive the climate, if one cannot correctly model the water cycle (which in turn will require modelling clouds), one cannot begin to expect to be able to model climate.
When the CGMs get the water cycle right not simply globally but on small scale regional basis, then it might be time to look at them and consider what they project. Until then, it is obvious that their output is simply garbage.

Throughout the year of 1959 I was on Christmas Island [1200 miles due south of Honolulu and 2deg N of the equator ] with my engineer regiment preparing for a series of H bomb tests that did not take place !
Prior to travelling I did a little research :
In 1892 James Morrison & Co,London obtained 21 year occupancy rights for coconut planting and pearl fishing .
This was not a success ,as in 1902 – 10 years into the lease – Lever Bros acquired the island and planted huge numbers of coconut palms .
In 1905 a Lever Bros expert seeded the lagoon with gold-lipped oysters from Thursday Island in the Torres Straits. However later that year there was a drought and 75% of the coconut was destroyed.
By 1906 the island was deserted .
If only they had had the benefit of your rainfall maps they would have seen that they were on the very edge of the ITCZ and developed accordingly , or not at all.
Incidentally by 1959 the oysters were doing well .I bought an aqualung and sold the shells I found in Honolulu while on leave . The income paid for the aqualung !

I do not want to derail or side track this post where Willis comments upon a known problem with the models
which models are hopeless at reproducing the water cycle on this water world which we inhabit, but apparently UAH is not reading the current El NIno as pushing up November temperatures.
October 2015 was indeed a warm month in the satellite record, and many predicted that due to lag temperatures would remain high (and increase) for the next 4 to 6 months, but so far in November (first 12 days), temperatures have been falling back from the October figure.
ENSO is still showing up strongly in the ENSO meter (just under +2.5C), and, of course, it is too early to say how November will pan out in the UAH and RSS data sets, but it would be ‘useful’ if the November satellite data (which no doubt will be available early December) adds some realism and adds a contrarian standpoint to the claims that 2015 is the warmest year ever on record.
See: http://notrickszone.com/#sthash.kT65AL9n.dpbs
Sorry if I am diverting attention, but I thought that those who are interested in current events and the present cycle might like to know how November is presently shaping up.

The problem appears to be that the model is garbage. It is not only that its representation of ITCZ is too symmetrical, but in another respect lacks symmetry altogether, in spite of the fact that annual hemispheric average absorbed radiation is the same for North and South. It is not so in models, only in reality.
Looks like an important symmetry can only be established on an asymmetric object by a symmetry breaking process, that is, albedo is a strictly regulated quantity and a single ITCZ is part of this regulation loop. The details of this regulation are not understood at all, so no wonder computational climate models miss it completely.Journal of Climate, Volume 26, Issue 2 (January 2013) pp. 468–477.
doi: http://dx.doi.org/10.1175/JCLI-D-12-00132.1The Observed Hemispheric Symmetry in Reflected Shortwave IrradianceAiko Voigt, Bjorn Stevens, Jürgen Bader and Thorsten Mauritsen

It is always interesting to see your findings. I have some questions about what model is chosen. It is important for me to know a little more about the model. Every model will have different results, and every model will have its shortcomings. Which of the models are most representative? It would be interesting to see if the same picture came up with a GISS-model (the preferred choice of some of the climatists). I see that modelers are aware of many of the shortcomings of the models , as you can see from the discussion in this paper:
Changes in precipitation extremes over Eastern China simulated by the Beijing Climate Center Climate System Model (BCC_CSM1.0) Li Zhang*, Min Dong, Tongwen Wu Beijing Climate Center, China Meteorological Administration, Beijing, PR China
4. DISCUSSION Based on daily precipitation during 1956−2009 from 349 rain gauge stations in eastern China, the performance of the BCC_CSM1.0 in simulating the regional extreme precipitation change over eastern China (east of 105° E) is evaluated. We also obtain similar results as previous research about the observation (e.g. Pan 2002, Zhai et al. 2005, Dong et al. 2011), such as the frequency and amount of extreme precipitation decreased in Northeast and North China, and increased in South China during the period of 1956 to 2009. The 20th century simulation forced by observed greenhouse gases, aerosols, solar irradiance and vol – canic aerosols shows that (1) BCC_CSM1.0 reproduces the basic feature of observed climatology of annual total and extreme precipitation over the 95th percentile, with spatial correlation of 0.77 for both of them, and (2) BCC_CSM1.0 captures the main change patterns of yearly accumulated extreme precipitations above the 95th percentile, although there are some model biases in the spatial extension and strength. There are also some model biases: (1) the annual to tal and extreme precipitation south of Yangtze River are underestimated. (2) The frequency of heavy precipitation (≥25.0 mm d–1) over eastern China is underestimated, but the frequency of light rain (0.1−10 mm d–1) is overestimated. (3) The longterm trends of annual extreme precipitation amount and frequency in the recent 50 yr are opposite to those from observations in North and South China. The model also has poor performance in simulating their interdecadal variations in North and South China. The regional differences for extreme precipitation change is considerable and there are interdecadal variation signals for extreme heavy precipitation. Although some encouraging results for extreme precipitation simulation are obtained, the performance of BCC_ CSM1.0 needs to be im proved. IPCC AR4 models also have large biases in simulation of precipitation resulting from the East Asian summer monsoon (EASM) (Sun & Ding 2008). It is therefore a common task for current climate system models to improve their performance in simulating the pre – cipitation over East Asia. A good simulation of the EASM is very important for precipitation simulation in East Asia. The probability of climate extremes is also strongly affected by atmo spheric circulation and climate variability, such as Madden−Julian Oscillation, El Niño−Southern Os – cillation, and Pacific interdecadal variability, in the global and/or regional scale (Jones et al. 2004, Curtis et al. 2007, Kenyon & Hegerl 2010). The climate system model working group in BCC are devoting themselves to improve the capability of BCC_CSM.

TRMM was a satellite radar transponder and infrared (and microwave) imager that measured rainfall radar returns and the corresponding heat signatures to “measure” rainfall rates. It ran out of fuel in April of this year. There is a follow-on mission called the Global Precipitation Mission (GPM), but I haven’t researched its capabilities. for information on the mission see: https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission

It can register rainfall. It does have problems with determining whether rainfall actually reaches the surface.
Dry air masses, especially warm ones evaporate a lot of precipitation before it can hit the ground. Either the rain rate overwhelms evaporation or the air reaches moisture saturation.

(While I would never presume to tell you how to spend your time …)
It might be interesting to see the results of a random selection of models. Just to confirm that bcc-csm1wasn’t an outlier.
It’s OK for ‘climate scientists’ to suggest that smaller grids are impossible right now but, as you seem to suggest here, without the detail everything rapidly degenerates into a fantasy world.
If, as you hypothesise, emergent, and relatively local phenomena, govern the the climate then not accurately modelling these features makes a mockery of CMIP5 or any other round of ‘inter-comparison’.
Related is the idea, from your previous post, that there are actually ‘formulae’ available to deal with (global!) evaporation and such. If modellers have simply sat in their ‘offices’ and randomly chosen a formula that they were fed with as under grads then it is little wonder that the models go ‘off the rails’ fairly quickly.
I just wish that they (climate modellers) would be a little more honest about those ‘slight problems’ when they come to brief Politicians and others outside the field.
__________________But the other way often leads to error as well. Worse than that, however, is that it leads to a hidebound view of the subject, one which has already closed a host of doors and ruled out a host of possibilities.
While I won’t go into details, I was once involved in a ‘heated discussion’ with a PhD (now tenured) Computer Scientist. “Why on earth would a phone need an IP address?” was the core of his argument. There are none so blind as … well, ‘specialists’.

Getting realistic, if one were charged with designing a control system that would keep an Earth sized object within +/-0.3 C for an extended period then if you could do that then I would gladly employ you.

Hey, another person who sounds like they have real-life experience with control systems. The stability of the earth’s climate system is astounding, given that it is only regulated by clouds and such. Thanks for the comment.
w.

As I see it, the basic problem with the various critics here complaining about Willis’ not reading the extant literature first is one of capacity.
Willis can do in hours or days what many critics here cannot in weeks, months, years or decades and it just urks the bejeezus out of them. If they were to try Willis’ “beginners mind” approach they might never get to the currently discussed issue. For them, reading the extant literature is the only possible way to catch up to the current state of the science and make a relevant contribution or comment (And yes, I cannot even do that as I am only a layman and not a brilliant one either).
Thus in their comments/criticisms they sound like bitter old University Professors.
Them what can do, them what can’t teach.

I see the basic problem as being the climastrologists claim that “The data doesn’t matter”; Willis analyses data and that, in their estimation, is fundamentally wrong. Climastrology is about as relevant as studying the sex habits of pink unicorns. Thus their comments/criticisms make them sound as if they are on potent psychedelic drugs.

As I often point out, unless we can get to grips with the water cycle and fully know and understand this including how it drives cloud formation in addition to evaporation and rainfall, we will never stand any prospect of modelling the climate and its future development.
In an interesting article posted on Jo Nova’s site is an article pointing out that evidence suggests that local temperatures are influenced by local rainfall conditions. A change in rainfall patterns can therefore lead to a change in temperature. What some might consider to be rising temperatures in some way correlated to rising CO2 could be nothing more than local temperature changes caused by slight changes in rainfall patterns.
See: http://joannenova.com.au/2015/11/blockbuster-are-hot-days-in-australia-mostly-due-to-low-rainfall-and-electronic-thermometers-not-co2/#more-46191

Willis, I apologise in advance as I haven’t got the time yet to read your always great post. But just looking at the short summary on the main WUWT page I just wanted to mention that 2 ‘ITCZ’s happen for real almost every year. And almost every year we get whats called ‘Twinning’. This is the presence of two tropical disturbances and sometimes tropical depressions and sometimes tropical cyclones. In fact ”Twinning’ is happening right now. This is not model speak this is real time satellite monitoring. I am a tropical forecaster and am quite busy now but I do want to read your article. I always learn something. Cheers from New Zealand.

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